Topic
Meta learning
About: Meta learning is a research topic. Over the lifetime, 160 publications have been published within this topic receiving 1078 citations.
Papers published on a yearly basis
Papers
Proceedings Article•
15 Feb 2018TL;DR: In this paper, the authors propose novel extensions of prototypical networks that are augmented with the ability to use unlabeled examples when producing prototypes, which is similar to our approach.
Abstract: In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We evaluate these methods on versions of the Omniglot and miniImageNet benchmarks, adapted to this new framework augmented with unlabeled examples. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would.
242 citations
TL;DR: A deep meta learning based model, entitled ST-MetaNet, which employs a sequence-to-sequence architecture, consisting of an encoder to learn historical information and a decoder to make predictions step by step to tackle challenges of predicting urban traffic.
Abstract: Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) spatial diversity of such spatio-temporal correlations, which varies from location to location and depends on the surrounding geographical information, e.g., points of interests and road networks; and 3) temporal diversity of such spatio-temporal correlations, which is highly influenced by dynamic traffic states. To tackle these challenges, we proposed a deep meta learning based model, entitled ST-MetaNet+, to collectively predict traffic in all locations at the same time. ST-MetaNet+ employs a sequence-to-sequence architecture, consisting of an encoder to learn historical information and a decoder to make predictions step by step. Specifically, the encoder and decoder have the same network structure, consisting of meta graph attention networks and meta recurrent neural networks, to capture diverse spatial correlations and diverse temporal correlations, respectively. Furthermore, the weights (parameters) of meta graph attention networks and meta recurrent networks are generated from the embeddings of geo-graph attributes and the traffic context learned from dynamic traffic states. Extensive experiments were conducted based on three real-world datasets to illustrate the effectiveness of ST-MetaNet+ beyond several state-of-the-art methods.
136 citations
9 Jul 2020
TL;DR: A novel framework termed as Federated Meta-Learning for fraud detection that enables banks to learn fraud detection model with the training data distributed on their own local database and achieves significantly higher performance compared with the other state-of-the-art approaches.
Abstract: Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make traditional model difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we introduce a novel framework termed as Federated Meta-Learning for fraud detection. Different from the traditional technologies trained with data centralized in the cloud, our model enables banks to learn fraud detection model with the training data distributed on their own local database. A shared whole model is constructed by aggregating locallycomputed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. To achieve the good performance of classification, we further formulate an improved triplet-like metric learning, and design a novel meta-learning-based classifier, which allows joint comparison with K negative samples in each mini-batch. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches.
99 citations
15 Dec 2020
TL;DR: Few-shot Classification of Aerial Scene Images via Meta-learning Pei Zhang 1-†, Yunpeng Bai 2,†, Dong Wang 1, Bendu Bai3 and Ying Li 1,* are presented.
Abstract: Few-shot Classification of Aerial Scene Images via Meta-learning Pei Zhang 1,†, Yunpeng Bai 2,†, Dong Wang 1, Bendu Bai3 and Ying Li 1,* 1 School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech & Image Information Processing, Northwestern Polytechnical University, Xi’an 710129, China; cszhangpei@mail.nwpu.edu.cn (P.Z.); dongwang@mail.nwpu.edu.cn (D.W.) 2 School of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia; yunpengb@student.unimelb.edu.au (Y.B.) 3 School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Shaanxi, Xi’an 710121, China; baibendu@xupt.edu.cn (B.B.) * Correspondence: lybyp@nwpu.edu.cn (Y.L).; Tel.: +86-029-8843-1532 † These authors contributed equally to this work. Version November 16, 2020 submitted to Remote Sens.
66 citations
Proceedings Article•
24 May 2019TL;DR: This paper develops a meta-algorithm bridging the gap between popular gradient-based meta-learning and classical regularization-based multi-task transfer methods, and is the first to simultaneously satisfy good sample efficiency guarantees in the convex setting and generalization bounds that improve with task-similarity.
Abstract: We study the problem of meta-learning through the lens of online convex optimization, developing a meta-algorithm bridging the gap between popular gradient-based meta-learning and classical regularization-based multi-task transfer methods. Our method is the first to simultaneously satisfy good sample efficiency guarantees in the convex setting, with generalization bounds that improve with task-similarity, while also being computationally scalable to modern deep learning architectures and the many-task setting. Despite its simplicity, the algorithm matches, up to a constant factor, a lower bound on the performance of any such parameter-transfer method under natural task similarity assumptions. We use experiments in both convex and deep learning settings to verify and demonstrate the applicability of our theory.
51 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 25 |
| 2020 | 31 |
| 2019 | 28 |
| 2018 | 23 |
| 2017 | 11 |
| 2016 | 3 |